Closure errors quantify the inconsistency of seemingly redundant interferograms. Systematic closure errors, associated with e.g. changes in soil moisture or vegetation, can bias estimated InSAR time series. Previous work has shown that the bias can be reduced by including interferograms spanning long temporal baselines. However, it is not clear how the bias reduction depends on the temporal signatures of the closure errors and in what circumstances including long-term interferograms improves or deteriorates the InSAR phase history and ultimately deformation estimates.

To identify how the temporal signatures relate to InSAR time series estimation, we introduce a mathematical framework that quantifies temporal closure signatures as a function of time and time scale. Technically speaking, we construct two complementary bases of the annihilator of the vector space of all temporally consistent phases, with each basis element extracting the closure error corresponding to the element's time and time scale. Applying this framework to Sentinel-1 observations, we find contrasting short-term, seasonal, and multi-annual closure signatures across land cover types. The inclusion of long-term interferograms is associated with characteristic changes in seasonal amplitudes and long-term trends in the InSAR phase history estimates. 

To determine when including long-term interferograms improves InSAR time series estimation, we formulate simple interferometric scattering models for seasonally variable soil moisture and vegetation conditions and sub-resolution deformation as is common in ice-rich permafrost. We find that including long-term interferograms improves the InSAR time series in simulation scenarios dominated by soil moisture wetting and dry down cycles. Conversely, including long-term interferograms can have a deleterious impact on InSAR time series estimates in scenarios with seasonal vegetation and sub-resolution deformation.

We conclude with simple diagnostics on how temporal closure signatures and expert knowledge can inform InSAR processing to maximize deformation time series quality for a range of geohazards, including lowland permafrost deformation, landslides, and sinkholes.

How to cite: Zwieback, S. and Biessel, R.: InSAR closure errors: temporal signatures and impact on deformation time series estimation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-308, https://doi.org/10.5194/egusphere-egu24-308, 2024.

Distributed scatter interferometric synthetic aperture radar (DS-InSAR) technology has been extensively employed for surface deformation monitoring, with phase optimization as a pivotal step. Currently, phase optimization techniques utilize the statistical intensity distribution of pixels to select homogeneous pixels. Pixels with low temporal intensity stability are excluded from consideration, avoiding their involvement in the phase optimization. However, it is noteworthy that distinguishing between homogeneous and heterogeneous pixels becomes more challenging in mountainous areas. Additionally, pixels with low stability are affected not only by thermal or environmental noise but also by the influence of local incidence angles, causing ground deformation beyond the Maximum Detectable Deformation Gradient (MDDG) of InSAR, resulting in geometric decorrelation. These pixels are often erroneously classified as noise and discarded. Nevertheless, these pixels contain rich and crucial deformation information, indicating disaster risks. Therefore, optimizing the phase of these pixels is essential.

This paper introduces a method for interferometric phase optimization of distributed scatterers in mountainous regions, considering geometric decorrelation (GD-DS). Using real InSAR differential interferometric phases as a basis, the study simulates interferometric phase datasets with rich spatiotemporal features, ensuring the correlation between simulated GD-DS phases and MDDG. Subsequently, K-means clustering is applied to segment the MDDG map, with resulting connected regions representing homogeneous pixels with similar local incidence angles. Convolutional denoising training is performed on homogeneous pixels using the generative adversarial network model (Pix2pix), and the trained model is then applied to real interferometric phase images. The proposed strategy and method are successfully applied to interferometric phase optimization in the Jishishan region of Gansu Province, China. Compared to traditional methods, the new approach demonstrates superior phase optimization performance, particularly in the case of GD-DS. Discussion and analysis of the spatial correlation between GD-DS and MDDG in the real experimental area confirm that introducing MDDG as a reference to optimize GD-DS is a key factor in improving phase optimization. Furthermore, the computational time of the new method is significantly reduced compared to traditional methods.

How to cite: Guo, A. and Sun, Q.: Interferometric Phase Optimization Method for Mountainous Regions Considering Geometric Decorrelation of Distributed Scatterers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15538, https://doi.org/10.5194/egusphere-egu24-15538, 2024.

One of the enduring facets within contemporary monitoring systems resides in the automation of data processing. This methodology ensures expeditious access to the most current and objectively derived results. Several systems for terrain monitoring have been realised in the last few years, with the leading role of the European Ground Motion Service (EGMS), part of the Copernicus program. Still most of these systems rely on the usage of the advanced Interferometric Synthetic Aperture Radar (InSAR) techniques which are not capable of exploring more dynamic and complex terrain change patterns as those related to underground mining works in Central Europe. A extensive study within the frames of the Polish realisation of the European Plate Observing System (EPOS) project, comprising long term monitoring between the years of 2016 and 2023 revealed the necessity of usage of the classical DIfferential InSAR (DInSAR) for more detailed study of the processes happening in the area of the Upper Silesian Coal Basin (USCB) in Poland. Within the project EPOS-PL+ we have developed an automated system for DInSAR processing of SAR data from Sentinel-1 satellite. The system also includes modules for processing of third party mission X-band data. 
This processing approach excels in managing significant deformations with reduced coherence, unlike methods relying on stable scatterers. The automated framework encompasses data retrieval, Line of Sight (LOS) deformation computation, trend elimination for atmospheric correction, and assessment of interferogram quality. The final step involves decomposing the LOS deformation into vertical and east-west components.
Upon initiation of the application, the user delineates parameters such as the region of interest by a shapefile, the period of study, and ascending and descending orbits. Subsequently, ingress into the Alaska Satellite Facility service repository, and data is procured for subsequent processing utilising the DInSAR method facilitated by the snappy library. This library enables script-based manipulation of the SNAP program using the Python language.
The subsequent phase involves detrending the data. Raw 1D deformation maps exhibit discernible trends, primarily attributable to atmospheric variations between successive acquisitions. To overcome this problem, a plane is fitted to the deformation data, and the estimated values are differentially subtracted from the original dataset. This estimation is implemented through two distinct methodologies. The more intricate approach include sthe identification of stable points based on nine coherence maps correlating with the deformation values, followed by the fitting of a plane. The simpler approach involves the fitting of a plane to the entire set of deformation data.
The quality check stage involves examining the dataset's pixels for coherence levels exceeding a set threshold (e.g., 0.2). Pixels failing coherence criteria are excluded, and linear interpolation is applied only to selected pixels. This approach minimizes phase unwrapping errors' propagation and effectively removes atmospheric effects in the final analysis. In instances of significant data gaps, the ensemble of adjacent images used for interpolation is expanded to reduce the impact of individual map errors. The enhanced DInSAR data are then projected into 2D components, namely the vertical and east-west (horizontal) dimensions.

How to cite: Teodorczyk, D., Ilieva, M., and Balak, P.: Service for automated processing and correction of DInSAR deformation maps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18703, https://doi.org/10.5194/egusphere-egu24-18703, 2024.

Interferometric synthetic aperture radar (InSAR) decorrelation that creates great challenges to phase unwrapping has been a critical issue for mapping large earthquake deformation. Some studies have proposed a “remove-and-return model” solution to tackle this problem, but it has not been fully validated yet, and therefore has rarely been applied to real earthquake scenarios. In this study, we use the 2023 Mw 7.8 and 7.6 earthquake doublet in Turkey and Syria as a case example to develop an iterative modeling method for InSAR-based coseismic mapping. We first derive surface deformation fields using Sentinel-1 offset tracking and Sentinel-2 optical image correlation, and invert them for an initial coseismic slip model, based on which we simulate InSAR coseismic phase measurements. We then remove the simulated phase from the actual Sentinel-1 phase and conduct unwrapping. The simulated phase is added back to the unwrapped phase to produce the final phase measurements. Comparing to the commonly-used unwrapping method, our proposed approach can significantly improve coherence and reduce phase gradients, enabling accurate InSAR measurements. Combining InSAR, offset tracking and optical image correlation, we implement a joint inversion to obtain an optimal coseismic slip model. Our model shows that slip on the Çardak Fault is concentrated on a ~100 km segment; to both ends, slip suddenly diminished. On the contrary, rupture on the East Anatolian Fault Zone propagated much longer as its geometry is fairly smooth. The iterative coseismic modeling method is proven efficient and can be easily applied to other continental earthquakes.

How to cite: Chen, J. and Zhou, Y.: Coseismic slip distribution of the 2023 earthquake doublet in Turkey and Syria from joint inversion of Sentinel-1 and Sentinel-2 data: An iterative modeling method for mapping large earthquake deformation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7005, https://doi.org/10.5194/egusphere-egu24-7005, 2024.

The fault structures of the 12 November 2017 Sarpol-e Zahab earthquake in Iran, as inferred from geodetic and geological data, exhibit significant distinctions, indicating intricate interactions between the crystalline basement and sedimentary cover. To further investigate this phenomenon, we employ interferometric synthetic aperture radar (InSAR) observations and 2-D Finite Element Models (FEM) with various fault geometries, such as planar, ramp-flat, and splay faults, to analyze mechanical (stress-driven) afterslip models for postseismic deformation. The kinematic coseismic slip model support a planar fault dipping at 15º, which is in good agreement with previously published results. Based on the coseismic model, we vary the fault geometries and explore the relationship between afterslip fault geometries and fault friction properties. We show that the planar frictional afterslip model fails to completely explain the long-wavelength postseismic deformation field. Instead, a ramp-flat fault model explains well the majority of the postseismic observations, with a maximum afterslip of approximately 1.0 m. The friction variations after fault strengthening are estimated to be about 0.001 and 0.0002 for the up-dip and down-dip portions, respectively. Expanding on the optimal ramp-flat fault model, we introduce an additional splay fault, which further improves the model fit, although the splay fault's frictional slip was limited to less than 0.2 m, and there is a trade-off between the splay fault geometries and their friction variations. Considering our results in conjunction with relocated aftershocks and geological cross-sections, we propose that a splay fault may have been weakly triggered after the mainshock, indicating more complex fault interactions than a simple decoupling layer between the basement and sedimentary cover.

How to cite: Guo, Z., Motagh, M., and Baes, M.: Structural Complexity Revealed by Frictional Afterslip Models and InSAR Observations Following the 2017 Mw 7.3 Sarpol- e Zahab (Iran-Iraq) Earthquake: Insights from Numerical Modeling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11944, https://doi.org/10.5194/egusphere-egu24-11944, 2024.

On July 1, 2022, a doublet of earthquakes, with a magnitude of Mw6.0 and a Mw5.7 aftershock between them, occurred within a two-hour period in the southeastern part of the Zagros Mountains near the Persian Gulf in Iran. This doublet earthquake event provides a unique opportunity to study the geometric properties of geological faults and the frictional attributes of rocks in the southeastern of the Zagros Mountains, particularly in the vicinity of the Hormoz salt layer. Here, we acquired co-seismic and post-seismic InSAR ascending and descending observations to simultaneously determine fault geometry and slip distribution models for the doublet earthquakes based on Bayesian inference. The inversion results reveal that the doublet earthquakes occurred on two distinct faults with similar strike (101.93°, 93.7°) but notable differences in dip (56.2°, 31.3°), and the slip distribution of the mainshock 2 is shallower and more westward compared to the mainshock 1. Moreover, the reliability of the fault geometry and slip distribution was confirmed through detailed discussions on the distributions of post-seismic kinematic afterslip, the relocated aftershocks beyond five months after the mainshocks, and the changes in positive Coulomb stress triggered by co-seismic events. Additionally, our post-seismic deformation modeling elucidated that post-seismic deformation is predominantly driven by stress induced by co-seismic event, accompanied by the release of both afterslip and aftershocks. Afterslip is distributed both up- and down-dip of the coseismic region on the two faults, with the maximum afterslip concentrated in the shallow portions, reaching approximately 0.45 m. By comparing the temporal evolution characteristics of stress-driven afterslip distributions with those of kinematic afterslip, we observed significant inconsistencies in the frictional properties within the southeastern Zagros Mountains, particularly between the Hormoz salt layer and its upper region. Specifically, above the Hormoz salt layer, the friction is stronger, and the relaxation time of afterslip is shorter. Finally, we also discussed the triggering potential of the mainshock 1 and the Mw5.7 aftershocks on mainshock 2, and from the perspective of Coulomb stress transfer, we found that mainshock 1 and Mw5.7 aftershocks may have triggering effects on mainshock 2.

How to cite: zhao, X., dahm, T., and xu, C.: Fault Slip Distribution and Inhomogeneous Frictional Properties in the Southeastern Zagros Mountains of the 2022 Iran doublet Earthquakes Inferred from Bayesian Inference and InSAR observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6273, https://doi.org/10.5194/egusphere-egu24-6273, 2024.

The geometric complexity of strike-slip faults, such as stepovers, bends and branches, is a pivotal indicator of segmented fault rupture. These features act as barriers to the activity of strike-slip faults, leading to uneven stress distribution along the fault zone, thereby influencing the initiation, propagation, and termination of earthquake ruptures.

The East Anatolian Fault (EAF) is a significant sinistral strike-slip fault, connecting with the North Anatolian Fault (NAF) to the north and the Dead Sea Fault (DSF) to the south. Located in southeastern Turkey, it plays a crucial role in accommodating the relative motion between the northward-moving Arabian Plate and the westward-moving Anatolian Block. Despite the relative quietude of the EAF since the beginning of the 20th century, historical seismic activity indicates its potential to generate devastating earthquakes, as evidenced by a series of relatively large earthquakes occurring between 1822 and 1905. On January 24, 2020, the Pütürge segment at the northeastern end of the EAF experienced the Mw6.8 Elaziğ earthquake (2020 event). Subsequently, on February 6, 2023, the southwestern segment of the EAF witnessed the Turkey–Syria Earthquake Sequence (2023 event). The consecutive occurrence of these two seismic events has provided an opportunity to investigate the tectonic activity characteristics and seismic triggering relationships of the EAF.

In this study, we take the two events that occurred on the EAF in 2020 and 2023 as the time nodes and take the EAF as the research object. Utilizing InSAR technology, the research investigated the deformation during seismic cycles (interseismic, coseismic, and postseismic) based on Sentinel-1 radar data. We computed interseismic deformation velocities from 2015 to 2020 and displacement time series from February 2020 to February 2023, as well as from February 2023 to September 2023. Subsequently, this study extensively considered the geometric complexity of faults and established an elastic triangular dislocation model. Based on this model, we derived the interseismic fault slip distribution for the EAF from 2015 to 2020, as well as coseismic and postseismic fault slip distributions for the 2020 and 2023 events. The results indicate that: 1) The slip rate along the EAF exhibits a decreasing trend from the northeastern end (5 mm/yr) to the southwestern end (2 mm/yr); 2) The interseismic slip deficits of the EAF correlate well with the coseismic fault slip distribution of the 2020 and 2023 events; 3) The postseismic fault slip following the 2020 and 2023 events primarily occurs at coseismic slip deficit areas.

How to cite: Yu, C., Han, B., Song, C., Li, Z., and Aoki, Y.: Fault irregularity of recent large strike slip earthquakes revealed by satellite imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8695, https://doi.org/10.5194/egusphere-egu24-8695, 2024.

The ionosphere, located 50 km above the Earth's surface is characterized by ionization processes that can significantly impact electromagnetic signals within the microwave wavelength range. The magnitude of the impact depends on the density of free electrons, which have daily and seasonal oscillations but are also tied to the 11-year solar activity cycles. Radar signals are delayed after interacting with free electrons and ions, and the magnitude of such delay is inversely proportional to the radar frequency. Thus, sensors operating in the longer wavelength L-band are more affected than those operating in the C-band. However, even C-band interferograms can be significantly affected if the region is close to the geomagnetic equator.

The Central Andes in Northwestern Argentina, being in proximity to the geomagnetic equator, offer an excellent setting to study the impact of the ionosphere on interferograms. Its low vegetation cover results in highly coherent interferograms, and predominantly dry conditions at high elevations lead to small tropospheric disturbances.

We employ the split spectrum technique extended to time series analysis to identify interferograms that are impacted by ionospheric contributions. Subsequently, we apply statistical methods to those time series to recognize acquisitions more likely to be contaminated by the ionosphere.  The magnitude of ionospheric contribution is compared to tropospheric delay. We demonstrate the impact of the high-solar activity on interferograms by correlating our time series of ionospheric delay to sunspot activity and total electron content maps. The analysis of Sentinel 1 C-band data from both ascending and descending tracks reveals a more significant contribution in ascending passes in response to the daily cycle of free electron density. These findings prove the relevance of the ionosphere as source of disturbance in interferograms from Sentinel C-band, particularly for studies at the regional scale.

How to cite: Viotto, S., Bookhagen, B., Toyos, G., and Torrusio, S.: Ionospheric and Tropospheric Impact for InSAR Time Series Analysis in the Central Andes: A Case Study from Northwestern Argentina, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14887, https://doi.org/10.5194/egusphere-egu24-14887, 2024.

Concise and informative target feature descriptions are curial for accurate land cover classification of polarimetric synthetic aperture radar (PolSAR) images. Effective feature selection strategy significantly impacts both classification model design and final accuracy.

Ideally, target features should capture diverse polarization scattering characteristics and physical properties, the foundation for PolSAR image interpretation, and also require all these features satisfy the independent and identically distributed hypothesis, as directly using all these features can lead to sparse sample data in the multi-dimensional space, especially with limited samples, hindering model training.

To this end, existing research attempt to utilize multiple features manually or analyze specific scattering characteristics for classification scenarios. However, these studies mainly focus on manual feature selection or using traditional random forest importance-based feature selection strategy, adaptive feature selection tailored to individual situations remains less explored.

In order to address this gap, we propose a novel target-oriented feature selection framework leveraging multi-scale two-dimensional structural similarity measure (MTSSIM). This framework adaptively selects informative features from an initial PolSAR image feature set, encompassing commonly used polarization scattering features, spatial neighbor context features, and morphological features. The core principle lies in designing an efficient algorithm that selects features maximizing intra-class and minimizing inter-class structural similarity.

For enhanced robustness and practicality, the proposed framework incorporates two key modules: 1) Two-dimensional structural similarity representation: This module quantifies the structural similarity between two samples, and 2) Multi-scale feature structural similarity measurement: This module utilizes local feature images at multiple spatial neighborhood scales to assess the intra-class and inter-class structural similarity of each feature relative to the target category.

To validate the effectiveness of the proposed framework, we conducted classification experiments on two real PolSAR image datasets using identical classification methods and parameters for three feature sets: the manually chosen features that commonly used in PolSAR image classification task (Manual feature set), the random forest importance-based features (RF feature set), and MTSSIM-recommended features (MTSSIM feature set).

Experimental results demonstrate that the proposed MTSSIM feature set consistently outperforms traditional approaches, demonstrating significant improvements in classification accuracy. These benefits include: 1) Reduced misclassification rates: MTSSIM significantly decreases misclassified pixels, leading to more accurate and reliable land cover maps; 2) Enhanced homogeneity: MTSSIM-derived feature sets yield spatially consistent and less noisy classification results, facilitating easier interpretation and analysis. 3) Improved performance in small-sample scenarios: MTSSIM effectively utilizes limited data, enabling accurate classification even with limited training samples.

In conclusion, the MTSSIM framework offers a powerful and practical solution for optimizing feature selection in PolSAR image classification. By addressing feature redundancy and leveraging structural information, MTSSIM improves classification accuracy, making it a valuable tool for enhancing remote sensing applications in land cover mapping, environmental monitoring, and various other domains.

How to cite: Nie, W., Yang, J., Zhang, C., and Qi, Y.: A Target-oriented Feature Selection Framework for Polarimetric SAR Image Classification Based on Multi-scale Two-dimensional Structural Similarity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8174, https://doi.org/10.5194/egusphere-egu24-8174, 2024.

Climate change has led to increasingly serious flooding in many regions around the world, including Vietnam. The Kon and Ky Lo river basins in Binh Dinh and Phu Yen provinces, Vietnam, have experienced increasingly serious flooding in recent years, which has caused damage to property and people living in the area. This basin lacks the availability of historical flood maps; flood information is mainly in the form of statistical information on people's damage situations.

Floods in Vietnam often appear in the last months of the year, combined with storms and heavy rain, leading to more serious flooding. During such periods of heavy rain, measuring and monitoring the flood situation is very difficult. Currently, with the development of the European Space Agency's (ESA) radar Sentinel-1 remote sensing technology, flood monitoring has become more convenient compared to the use of optical remote sensing technology. Moreover, combined with Google Engine (GE) technology, mapping historical floods became easier.

In this study, we applied the Sentinel-1 satellite images on the GE platform combined with SRTM digital elevation model data to conduct flood mapping for the large floods of 2016 and 2021 along the Kon and Ky Lo rivers. These historical flood maps will be used on the basis of flood risk assessment and as a basis for assessing the accuracy of future hydrological-hydraulic flood simulation models. In addition, the study also delineated areas that are frequently flooded to help local authorities more easily manage disasters and have better response solutions in the future, in order to limit the risk of damage to people in areas affected by flooding.

How to cite: Van Phan, T., Anh Ngo, T., and Willems, P.: Historical Flood Mapping Combining Radar Remote Sensing and Google Engine Technologies for The Kon and Ky Lo River Basin, South Center Coast Vietnam, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4056, https://doi.org/10.5194/egusphere-egu24-4056, 2024.

India’s long 7500 km coastline covers vast habitats of rich biodiversity and occupants of about 26% of the country’s population in the coastal zone. By 2060, about 60 million of India’s coastal population will be exposed to a low-elevation coastal zone (LECZ) due to global mean sea level rise (GMSL) and its associated hazards (Neumann et al., 2015). However, the low-lying coastal zones are vulnerable to increasing sea levels and coastal subsidence, which threaten the region's socio-economic development (Shirzaei et al., 2021). The erosion assessment report depicts that 60% of the Gujarat shore covering 1600 km of extensive coastline in western India is undergoing erosion. This study investigates and analyzes the combined influence of coastal subsidence and SLR along the south of Gujarat shoreline. In that context, we explored the descending track (path 34) of C-band Sentinel-1 satellite data (92 SAR imageries) in interferometric wide swath mode (IW2 and IW3) of the European Space Agency (ESA) covering the study area from March 2020 to June 2023. The data sets were processed in an open-source GMTSAR software following an advanced Small BAseline Subset (SBAS) based Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique (Berardino et al., 2002). The results exhibit a deformation rate of  >5 mm/year in various parts of Gujarat's Surat, Bhavnagar, and Bharuch districts. Kododara region in Surat district shows a maximum deformation of >15 mm/year, Navamadhiya in Bhavnagar district is showing subsidence of >20 mm/year, and Chanchvel village in Bharuch is also showing subsidence of >15 mm/year. However, the precipitation data shows a total deviation of -3 % and -5 % in the monthly average compared to normal rainfall observed in Bharuch and Surat districts, respectively, from January 2000 to November 2023 (WRIS, India). Rapid urbanization, dependency on groundwater for basic needs and industrialization, and the impact of increasing sea levels could influence the coastal deformation and inundation hazard risk over the long shorelines and those coastal cities, which needs to be investigated in detail.

References

• Berardino, P., G. Fornaro, R. Lanari, and E. Sansosti. 2002. "A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms." IEEE Transactions on Geoscience and Remote Sensing 40 (11): 2375–83. https://doi.org/10.1109/TGRS.2002.803792.
• Neumann, B., et al. (2015). "Future coastal population growth and exposure to sea-level rise and coastal flooding-a global assessment." PloS one 10(3): e0118571.
• Shirzaei, M., et al. (2021). "Measuring, modelling and projecting coastal land subsidence." Nature Reviews Earth, Environment 2(1): 40-58.

How to cite: Sharma, S. and Ojha, C.: Exploring Sentinel-1 for Coastal subsidence monitoring along India’s Gujarat shore using MT-InSAR Technique, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1129, https://doi.org/10.5194/egusphere-egu24-1129, 2024.

Bangladesh, a riverine South Asian country with many Haor areas, is extremely vulnerable to flash flooding, which occurs primarily between the months of April and May (pre-monsoon). A Haor is a type of wetland ecosystem found in Bangladesh's northeastern region that is essentially a tectonically active shallow depression with a bowl or saucer shape where water flows from upstream basins. Floods and the resulting sediment have both positive and negative impacts on the affected Haor region, with broader implications for agricultural, water, fisheries, and other resource planning and management. However, till now there is no measurement or literature on the amount of sediment deposition caused by these flash flooding events. Threfore, the primary goal of this study was to determine the amount of incoming sediments associated with flash floods in Haor regions using remote sensing and to validate it in the field. The amount of incoming sediment associated with the flash flood that occurred in June 2020 was estimated for a selected region in the affected northeastern part of Bangladesh for this purpose. Using Sentinel-1 satellite images, interferometric techniques were used to create Digital Elevation Models (DEMs) of the pre and post-flood period of 2020. A total of eight Sentinel 1 A and Sentinel 1 B images covering the study area were collected from 22 July to 26 July 2019 to assess pre flood land conditions and from 21 July to 27 July 2020 to assess post flood land conditions. Our study revealed that the overall sediment deposition was found to be about 2.8 cm on average for the selected entire region. Furthermore, it has been observed that relatively less flashy areas gained sediment increase of about 7.3 cm on average within this one year interval, and relatively upstream areas with steep gradient gained 4.5 cm increase. Any anthropogenic interventions in this area should take into account the natural sediment distribution pattern and avoid impeding sediment spreading pathways, as sediment acts as a natural countermeasure to tectonic-subsidence of this area.

How to cite: Rahaman, Kh. M. A., Himel, F. R., Zannat, M., Shampa, S., and Murshed, S. B.: Assessment of Incoming Sediment with Flash Flood: A Case Study of the 2020 Flood in the Northeastern Part of Bangladesh using SAR Interferometry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18815, https://doi.org/10.5194/egusphere-egu24-18815, 2024.